Model baselines are essential benchmarks in machine learning and data science that provide a reference point for evaluating the performance of more complex models. A baseline model is typically simple, fast to build, and easy to interpret, allowing data scientists to gauge whether their advanced models are performing adequately. By establishing a baseline, practitioners can identify potential issues with data quality or model design if the baseline performs unexpectedly well or poorly. This foundational step not only aids in decision-making but also accelerates the iterative process of model development, ensuring that subsequent models are genuinely improving upon the baseline performance.