Hyperparameter tuning is the process of optimizing the settings that govern the behavior of a machine learning model. Unlike model parameters, which are learned from the data, hyperparameters are set before training begins. These settings can significantly influence the model's performance, making it crucial to find the right combination. To perform hyperparameter tuning, various techniques can be employed, such as grid search, random search, or Bayesian optimization. Each method involves testing different hyperparameter values and evaluating the model's performance using a validation dataset. The goal is to identify the best hyperparameters that lead to improved accuracy and generalization.