Hyperparameter tuning involves adjusting parameters of a machine learning model to optimize its performance. This process can be done manually or with automated algorithms. It is iterative, with different combinations of parameters tested to maximize the target metric, like accuracy. Cross-validation is often used to ensure the model generalizes well.