Model baselines are essential components in the development of machine learning models, serving as reference points for evaluating performance. A baseline model is typically a simple, straightforward model that establishes a benchmark against which more complex models can be compared. By assessing the performance of a baseline, data scientists can identify potential issues with data quality or model design. If a baseline model performs poorly, it may indicate problems that need addressing. Conversely, if a complex model fails to outperform the baseline, it suggests that further refinement is necessary in terms of algorithms, features, or preprocessing techniques.