Hyperparameter Optimization is the process of tuning the parameters that govern the training of machine learning models. Unlike model parameters, which are learned from the data, hyperparameters are set before the training begins and can significantly affect the model's performance. Common hyperparameters include the learning rate, batch size, and the number of layers in a neural network. Various techniques are used for hyperparameter optimization, such as grid search, random search, and Bayesian optimization. These methods help identify the best combination of hyperparameters to improve the model's accuracy and efficiency. Effective optimization can lead to better results in tasks like classification and regression, enhancing the overall performance of the model.