Minimum Description Length (MDL) is a principle in information theory that suggests the best model for a given set of data is the one that minimizes the total length of the description of the data and the model itself. Essentially, it balances the complexity of the model against how well it fits the data, promoting simpler models that still capture essential patterns. In practice, MDL can be used in various fields, including machine learning, statistics, and data compression. By applying this principle, researchers can avoid overfitting, ensuring that their models generalize well to new, unseen data while remaining as simple as possible.