Model evaluation is a critical component in the machine learning lifecycle, ensuring that models are not only high-performing during training but also provide reliable and accurate predictions in real-world applications. By quantifying an ML model's performance through methods like holdout and cross-validation, practitioners can determine the best model for a given problem. Evaluation metrics such as accuracy, precision, recall, and F1-score help assess a model's strengths and weaknesses, guiding improvements and ensuring optimal performance. Overfitting, a common issue where a model memorizes rather than generalizes data, can be identified and rectified through proper evaluation, preventing poor performance on new data. This process is vital for organizations using machine learning to achieve business objectives, as incorrect predictions can have significant consequences, particularly in sensitive industries like healthcare. Tools like Comet facilitate this evaluation by integrating into existing infrastructures, allowing teams to manage and optimize models effectively.