Model Testing is the process of evaluating a predictive model to ensure its accuracy and reliability. This involves using a separate dataset, known as the test set, to assess how well the model performs on unseen data. By comparing the model's predictions to actual outcomes, developers can identify any weaknesses or areas for improvement. During Model Testing, various metrics, such as accuracy, precision, and recall, are calculated to quantify the model's performance. This helps in determining whether the model is suitable for deployment in real-world applications. Effective testing is crucial for building trustworthy and robust models.