Bias and variance are two critical sources of error in machine learning models, where bias refers to the error due to overly simplistic assumptions in the learning algorithm, and variance refers to the error due to excessive sensitivity to fluctuations in the training data. The trade-off between bias and variance is crucial for model performance, as high bias can lead to underfitting, while high variance can lead to overfitting.