Tuning methods are techniques used to optimize the performance of models in machine learning. These methods adjust various parameters, known as hyperparameters, to improve the model's accuracy and efficiency. Common tuning methods include grid search, random search, and Bayesian optimization, each offering different strategies for exploring the hyperparameter space. Effective tuning can significantly enhance a model's predictive capabilities. By systematically testing combinations of hyperparameters, practitioners can identify the best settings for their specific dataset and problem. This process is crucial in fields like data science and artificial intelligence, where model performance directly impacts outcomes.