Version control for models is an essential practice in data science and machine learning, enabling teams to track and manage changes to their models over time. Just as software developers use version control systems like Git to manage code, data scientists need similar tools to handle the complexities of model development. This includes tracking different versions of models, configurations, and datasets, which is crucial for reproducibility and collaboration. By implementing version control, teams can easily identify changes, roll back to previous versions if needed, and ensure that all members are aligned, ultimately leading to more efficient workflows and improved model performance.