Model validation is the process of assessing how well a statistical or machine learning model performs in making predictions. It involves comparing the model's predictions against actual outcomes to ensure accuracy and reliability. This step is crucial because it helps identify any weaknesses or biases in the model, allowing for improvements before it is used in real-world applications. During model validation, techniques such as cross-validation and performance metrics like accuracy, precision, and recall are often employed. These methods help determine if the model can generalize well to new, unseen data. Ultimately, effective model validation ensures that the model is trustworthy and can provide valuable insights.