The bias-variance tradeoff is a fundamental concept in machine learning that addresses the balance between two types of errors that affect model performance:

1. Bias: Error due to overly simplistic models that fail to capture the underlying patterns in the data, leading to underfitting.
2. Variance: Error due to overly complex models that capture noise in the training data, leading to overfitting.

The tradeoff is about finding the right complexity for the model where both bias and variance are minimized, ensuring good performance on both training and unseen data. This balance is often achieved through techniques like cross-validation, regularization, and choosing the appropriate model complexity.