The bias-variance tradeoff is a fundamental concept in machine learning that refers to the tradeoff between model simplicity and generalization performance. Bias refers to the error that is introduced by approximating a real-world problem with a simple model. Variance, on the other hand, refers to the error that is introduced by using a complex model that is sensitive to small fluctuations in the training data. A model with high bias will have low variance and will underfit the data, while a model with high variance will have low bias and will overfit the data. The goal is to find the right balance between bias and variance to achieve good generalization performance.