The Bias-Variance Tradeoff is a fundamental concept in machine learning that describes the balance between two types of errors that affect model performance. Bias refers to the error introduced by approximating a real-world problem, which can lead to oversimplified models that miss important patterns. This often results in underfitting, where the model performs poorly on both training and test data. On the other hand, Variance refers to the model's sensitivity to fluctuations in the training data. High variance can lead to overfitting, where the model captures noise instead of the underlying data distribution, resulting in poor generalization to new data. The tradeoff involves finding a model complexity that minimizes both bias and variance for optimal performance.