Hyperparameter Tuning is the process of optimizing the parameters that control the learning process of machine learning algorithms. Unlike model parameters, which are learned from the data, hyperparameters are set before the learning process begins and govern the overall behavior of the model, such as learning rate, regularization strength, and the number of trees in a random forest. Tuning involves searching for the best combination of hyperparameters that yield the highest model performance. Common techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization. Effective hyperparameter tuning can significantly enhance the accuracy and generalization of machine learning models.