+The Bias Variance Tradeoff is a central challenge in [Machine Learning](/wiki/machine_learning), representing the inherent tension between a model's ability to learn complex patterns and its capacity to generalize. A high [Bias](/wiki/bias) leads to oversimplified models that underfit, failing to capture true relationships, while high [Variance](/wiki/variance) results in overly complex models that overfit, learning noise from the training data. Finding the optimal balance between these two errors is crucial for building effective predictive [Model](/wiki/model)s.
+## See also
+- [Overfitting](/wiki/overfitting)
+- [Underfitting](/wiki/underfitting)
+- [Regularization](/wiki/regularization)
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