Nested cross-validation nests two loops: an inner one to tune the hyperparameters, an outer one to evaluate the model tuned this way. It provides a performance estimate that is not biased by the selection of hyperparameters, which a plain grid search result tends to overestimate.
Example
cross_val_score(GridSearchCV(pipe, grille, cv=3), X, y, cv=5)