Complexity penalty refers to the mechanism of discouraging overly complex models by penalizing additional parameters or intricate structures, thereby fostering simpler, more generalizable solutions. This concept is crucial in machine learning for balancing the trade-off between fitting the training data and maintaining predictive accuracy on unseen data.