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 complex model fails to outperform the baseline, it may indicate problems with the algorithm, features, or hyperparameters. Overall, baseline models play a crucial role in ensuring that advanced models provide meaningful improvements over simpler approaches.