The bias-variance tradeoff is a key concept in machine learning that refers to the balance between the model's complexity (bias) and its ability to generalize to unseen data (variance). High bias leads to underfitting, where the model oversimplifies and performs poorly. High variance leads to overfitting, where the model learns noise in the training data. Achieving the right balance is crucial for optimal performance.