Final answer:
MDL performance is a strong predictor of a model's ability to generalize to unseen data, balancing model complexity and data fitting to prevent overfitting. It is key in model selection within machine learning and statistics.
Step-by-step explanation:
The term MDL, which stands for Minimum Description Length, is a principle from the field of information theory and computer science that is often used when discussing model selection in machine learning and statistics. Essentially, the principle states that the best hypothesis for a given set of data is the one that leads to the best compression of the data. This involves finding a balance between the complexity of the model and its ability to fit the data without overfitting.
Thus, MDL performance is a strong predictor of how well a model generalizes to unseen data. It measures the ability of a model not only to capture the regularities in the data it was trained on but also to predict or describe new, unseen datasets efficiently. MDL can be particularly useful in situations where there is a need to choose between multiple models of varying complexity, helping to prevent the selection of an overly complex model that might not generalize well beyond the training data.