Summarize the process of model validation.
Model validation is the process of assessing the performance of a machine learning model to ensure it generalizes well to unseen data. This typically involves splitting the dataset into training and validation sets and using various metrics to evaluate the model's accuracy, precision, and recall. Techniques such as cross-validation may also be employed to provide a more robust assessment, helping to identify overfitting and ensuring the model's reliability in real-world applications.