The bias-variance tradeoff describes the balance a model must strike between being too simple (high bias, underfitting) and too complex (high variance, overfitting). A model with high bias misses real patterns, like a student who studies only summaries and fails nuanced questions. A model with high variance memorizes noise in training data and fails on new inputs, like a student who memorizes answers but cannot handle a differently worded exam. Good modeling finds the sweet spot.