Explain the bias-variance tradeoff.

Bias is error from overly simplistic assumptions (underfitting — the model misses real patterns). Variance is error from over-sensitivity to the training data (overfitting — the model changes a lot with small data changes). Increasing model complexity usually lowers bias but raises variance, and vice versa. The goal is the sweet spot that minimises total error on unseen data. Techniques like regularisation and ensembling help manage the balance.