Version control for models is a critical practice in machine learning and data science that involves tracking and managing changes to machine learning models over time. This process allows data teams to document modifications, ensuring reproducibility and facilitating collaboration among team members. By employing version control systems, teams can easily identify what changes were made, when, and by whom, which is essential for diagnosing issues and improving model performance. Additionally, version control enables quick rollbacks to previous model versions, safeguarding against potential regressions and enhancing the overall efficiency of the model deployment process.