Model Evaluation is the process of assessing how well a machine learning model performs. This involves using various metrics and techniques to measure the model's accuracy, precision, recall, F1 score, and other relevant performance indicators. Evaluation is typically done on a separate validation or test dataset that was not used during training to ensure an unbiased assessment of the model's capabilities. Techniques like cross-validation, confusion matrices, and ROC curves are often used to evaluate models. Proper model evaluation helps in selecting the best model, understanding its strengths and weaknesses, and ensuring its reliability in real-world applications.