What is hyperparameter tuning in Scikit-Learn?

Hyperparameter tuning is the process of finding optimal model configuration settings.

Unlike parameters learned during training, hyperparameters are set before training and control model behavior (e.g., depth of tree, learning rate). Scikit-Learn provides GridSearchCV and RandomizedSearchCV to systematically explore combinations and evaluate performance using cross-validation.