GridSearchCV

GridSearchCV is a class in the scikit-learn library, specifically in the model_selection module, that automates exhaustive search over a specified parameter grid for an estimator. Its purpose is to identify the combination of hyperparameters that yields the best model performance according to a chosen scoring metric, using cross-validation to assess generalization.

The user provides an estimator (for example, a support vector machine or a random forest) and a parameter grid that maps parameter names to lists of values. GridSearchCV evaluates all possible combinations of these values, performing cross-validation with a defined cv strategy. For each combination, it trains models on training folds and evaluates them on validation folds, aggregating scores across folds. After fitting, it exposes attributes such as best_params_, best_estimator_, best_score_, and cv_results_ to summarize the results. The best_estimator_ is generally refit on the full training data if refit is True (the default), making it ready for immediate predictions.

GridSearchCV supports pipelines, allowing simultaneous tuning of preprocessing steps and estimator parameters. It accepts a scoring parameter for custom metrics and can leverage the n_jobs parameter to parallelize computations across CPU cores. Cross-validation strategy can be customized via cv, enabling options like K-fold, stratified variants, or custom splitters.

Limitations include computational cost, as the search is exhaustive and scales with the size of the parameter grid and the dataset. For large grids or datasets, alternatives like RandomizedSearchCV or Bayesian optimization may be more practical. Overall, GridSearchCV provides transparent, repeatable model selection and a convenient way to identify robust hyperparameters.