entirely in the test setin[ ]mglearn plots plot_label_kfold(figure - label-dependent splitting with groupkfold there are more splitting strategies for cross-validation in scikit-learnwhich allow for an even greater variety of use cases (you can find these in the scikit-learn user guidehoweverthe standard kfoldstratifiedkfoldand groupkfold are by far the most commonly used ones grid search now that we know how to evaluate how well model generalizeswe can take the next step and improve the model' generalization performance by tuning its parameters we discussed the parameter settings of many of the algorithms in scikit-learn in and and it is important to understand what the parameters mean before trying to adjust them finding the values of the important parameters of model (the ones that provide the best generalization performanceis tricky taskbut necessary for almost all models and datasets because it is such common taskthere are standard methods in scikit-learn to help you with it the most commonly used method is grid searchwhich basically means trying all possible combinations of the parameters of interest consider the case of kernel svm with an rbf (radial basis functionkernelas implemented in the svc class as we discussed in there are two important parametersthe kernel bandwidthgammaand the regularization parameterc say we want to try the values and for the parameter cand the same for gamma because we have six different settings for and gamma that we want to trywe have combinations of parameters in total looking at all possible combinations creates table (or gridof parameter settings for the svmas shown here model evaluation and improvement