The bias-variance tradeoff in machine learning refers to the balance between a model's sensitivity to the training data (variance) and its tendency to make incorrect assumptions about the data (bias). High bias leads to underfitting, while high variance leads to overfitting. The goal is to find a model that generalizes well to unseen data, meaning it's neither too simple (high bias) nor too complex (high variance).