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Overfitting occurs when a model is too complex, characterized by low bias and high variance, and captures noise as if it were true underlying pattern, which harms the model's performance on new data.

Regularization is a technique used to prevent overfitting by discouraging overly complex models in machine learning. It does this by adding a penalty term to the loss function.

Variance is the error due to too much complexity in the learning algorithm. High variance can cause the model to model the random noise in the training data (overfitting).

Cross-validation is a method used to estimate the model's ability to generalize to an independent data set. It helps in determining how the model performs against unseen data and in combating overfitting.

The bias-variance tradeoff is the balance between the accuracy of the model on the training data and its ability to generalize well to unseen data. Models often face a tradeoff between bias and variance, seeking to minimize both to achieve good predictive performance.

Model complexity refers to the number of parameters in the model or the structure of the model. An increase in complexity generally leads to low bias and high variance, while a decrease simplifies the model but may increase bias.

Underfitting occurs when a model is too simple, characterized by high bias and low variance, and fails to capture underlying patterns of the data, leading to poor predictive performance.