Hyperparameter tuning is a critical process in enhancing the performance of machine learning and deep learning models by selecting the optimal combination of hyperparameters, which are not directly estimated from data. The article elaborates on the distinction between model parameters and hyperparameters, emphasizing the importance of hyperparameter tuning in achieving optimal model results. It explores manual and automated methods for hyperparameter tuning, detailing popular techniques like random search, grid search, Bayesian optimization, and Tree-structured Parzen estimators, among others. Various tools and libraries, such as Scikit-learn, Optuna, Hyperopt, and Ray Tune, are highlighted for their role in facilitating hyperparameter optimization. The article provides insights into different algorithms and tools that enhance the tuning process, while also offering resources for further exploration, including examples across various machine learning frameworks. Overall, the guide underscores the significance of understanding hyperparameter tuning as an integral part of machine learning projects to maximize model efficiency and performance.