Hyperparameter tuning is the process of finding the optimal values for hyperparameters in a machine learning model. Hyperparameters are parameters that are set before training and affect the learning process. Examples include learning rate, number of hidden layers, and regularization strength. Hyperparameter tuning aims to find the best combination of values that maximizes the model's performance and generalization ability.