variedin[ ]results pd dataframe(grid_search cv_results_we display the transposed table so that it better fits on the pagedisplay(results tout[ ]param_c param_gamma nan nan nan nan param_kernel rbf rbf rbf rbf linear linear linear linear params { kernelrbfgamma { kernelrbfgamma { kernelrbfgamma { { kernelrbfkernelgamma linear{ kernellinear{ kernellinear{ kernellinearmean_test_score rank_test_score split _test_score split _test_score split _test_score split _test_score split _test_score std_test_score rows columns using different cross-validation strategies with grid search similarly to cross_val_scoregridsearchcv uses stratified -fold cross-validation by default for classificationand -fold cross-validation for regression howeveryou can also pass any cross-validation splitteras described in "more control over crossvalidationon page as the cv parameter in gridsearchcv in particularto get only single split into training and validation setyou can use shufflesplit or stratifiedshufflesplit with n_iter= this might be helpful for very large datasetsor very slow models nested cross-validation in the preceding exampleswe went from using single split of the data into trainingvalidationand test sets to splitting the data into training and test sets and then performing cross-validation on the training set but when using gridsearchcv as model evaluation and improvement