What is a hyperparameter search?

Hyperparameter search (or tuning) is the process of finding the optimal hyperparameter configuration for a model. Grid Search exhaustively tests all combinations in a predefined grid — thorough but expensive. Random Search randomly samples combinations — more efficient than grid search for high-dimensional hyperparameter spaces. Bayesian Optimization builds a probabilistic model of the objective function and uses it to select the most promising hyperparameter values, converging faster than random search. Tools: Scikit-learn's GridSearchCV, Optuna, Ray Tune.