Hyperparameter tuning is the process of selecting the optimal configuration settings for a machine learning model before training begins — settings like learning rate, batch size, number of layers, and dropout rate. Unlike model parameters that the algorithm learns automatically, hyperparameters must be set by the practitioner. Grid search, random search, and Bayesian optimization are common methods. Finding good hyperparameters can mean the difference between a model that barely works and one that excels.