Hyperparameter tuning is the process of selecting the best set of parameters that control the behavior of a machine learning algorithm but are not learned from the data. Unlike model parameters (e.g., weights in a neural network), hyperparameters are set before training and can significantly impact performance. Examples include learning rate, batch size, number of layers, and regularization strength.

Tuning involves defining a search space and evaluating model performance across different combinations, typically using cross-validation. Common strategies include grid search (exhaustively trying all combinations), random search (sampling combinations randomly), and more advanced methods like Bayesian optimization, Hyperband, or evolutionary algorithms, which aim to explore the space more efficiently.

Good hyperparameter tuning improves generalization and prevents issues like overfitting or underfitting. It’s especially critical in deep learning and ensemble methods, where models are sensitive to these settings. However, it can be computationally expensive, so it's often balanced with resource constraints using parallelism or early stopping techniques.