GridSearchCV(estimator

, param_grid, cv=None, scoring=None, n_jobs=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False)

The GridSearchCV function is a method for tuning hyperparameters of a machine learning model. It is a way to automatically search for the best combination of hyperparameters in a specified range or set of values. The function takes an estimator object, a dictionary of hyperparameters to be tuned, and other optional parameters such as cross-validation folds, scoring metric, number of parallel jobs, and verbosity level. It returns a GridSearchCV object that can be used to fit the model and evaluate the results. The GridSearchCV function performs an exhaustive search over all possible combinations of hyperparameters, and returns the best combination that maximizes the scoring metric. The function can be used with any estimator that implements the scikit-learn estimator interface.