diff --git a/modules/ml/src/svm.cpp b/modules/ml/src/svm.cpp
index 8bed117639..449eb8dcd4 100644
--- a/modules/ml/src/svm.cpp
+++ b/modules/ml/src/svm.cpp
@@ -1669,13 +1669,13 @@ public:
         Mat samples = data->getTrainSamples();
         Mat responses;
         bool is_classification = false;
-        Mat class_labels0 = class_labels;
         int class_count = (int)class_labels.total();
 
         if( svmType == C_SVC || svmType == NU_SVC )
         {
             responses = data->getTrainNormCatResponses();
             class_labels = data->getClassLabels();
+            class_count = (int)class_labels.total();
             is_classification = true;
 
             vector<int> temp_class_labels;
@@ -1755,8 +1755,9 @@ public:
         Mat temp_train_responses(train_sample_count, 1, rtype);
         Mat temp_test_responses;
 
+        // If grid.minVal == grid.maxVal, this will allow one and only one pass through the loop with params.var = grid.minVal.
         #define FOR_IN_GRID(var, grid) \
-            for( params.var = grid.minVal; params.var == grid.minVal || params.var < grid.maxVal; params.var *= grid.logStep )
+            for( params.var = grid.minVal; params.var == grid.minVal || params.var < grid.maxVal; params.var = (grid.minVal == grid.maxVal) ? grid.maxVal + 1 : params.var * grid.logStep )
 
         FOR_IN_GRID(C, C_grid)
         FOR_IN_GRID(gamma, gamma_grid)
@@ -1814,7 +1815,6 @@ public:
         }
 
         params = best_params;
-        class_labels = class_labels0;
         return do_train( samples, responses );
     }
 
diff --git a/modules/ml/test/test_svmtrainauto.cpp b/modules/ml/test/test_svmtrainauto.cpp
new file mode 100644
index 0000000000..918d2b7117
--- /dev/null
+++ b/modules/ml/test/test_svmtrainauto.cpp
@@ -0,0 +1,89 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "test_precomp.hpp"
+
+using namespace cv;
+using namespace std;
+using cv::ml::SVM;
+using cv::ml::TrainData;
+
+//--------------------------------------------------------------------------------------------
+class CV_SVMTrainAutoTest : public cvtest::BaseTest {
+public:
+    CV_SVMTrainAutoTest() {}
+protected:
+    virtual void run( int start_from );
+};
+
+void CV_SVMTrainAutoTest::run( int /*start_from*/ )
+{
+    int datasize = 100;
+    cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
+    cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
+
+    RNG rng(0);
+    for (int i = 0; i < datasize; ++i)
+    {
+        int response = rng.uniform(0, 2);  // Random from {0, 1}.
+        samples.at<float>( i, 0 ) = rng.uniform(0.f, 0.5f) + response * 0.5f;
+        samples.at<float>( i, 1 ) = rng.uniform(0.f, 0.5f) + response * 0.5f;
+        responses.at<int>( i, 0 ) = response;
+    }
+
+    cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
+    cv::Ptr<SVM> svm = SVM::create();
+    svm->trainAuto( data, 10 );  // 2-fold cross validation.
+
+    float test_data0[2] = {0.25f, 0.25f};
+    cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
+    float result0 = svm->predict( test_point0 );
+    float test_data1[2] = {0.75f, 0.75f};
+    cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
+    float result1 = svm->predict( test_point1 );
+
+    if ( fabs( result0 - 0 ) > 0.001 || fabs( result1 - 1 ) > 0.001 )
+    {
+        ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
+    }
+}
+
+TEST(ML_SVM, trainauto) { CV_SVMTrainAutoTest test; test.safe_run(); }