Lantz's Law is a principle in the field of statistics and data analysis. It states that the more complex a model is, the more likely it is to fit the data well, but this can lead to overfitting. Overfitting occurs when a model captures noise instead of the underlying pattern, making it less effective for predicting new data. This law highlights the importance of balancing model complexity and simplicity. A simpler model may not fit the training data as closely, but it often generalizes better to unseen data. Thus, Lantz's Law serves as a reminder to prioritize predictive accuracy over mere data fitting.