MacCallum's Law is a principle in the field of statistics that states that the more complex a model becomes, the more likely it is to fit the data it is based on, but this does not guarantee that the model will accurately predict future outcomes. Essentially, it highlights the trade-off between model complexity and predictive power. This law serves as a cautionary reminder for researchers and analysts to avoid overfitting their models. Overfitting occurs when a model captures noise in the data rather than the underlying trend, leading to poor performance on new, unseen data.