Overfitting occurs when a machine learning model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. This results in poor performance when exposed to new, unseen data. To combat overfitting, we can employ various techniques:

1. Regularization: Regularization adds a penalty term to the model's loss function, discouraging large weights and reducing overfitting. L1 (Lasso) and L2 (Ridge) regularization are commonly used techniques.

2. Feature Selection: By selecting only the most relevant features, we can reduce overfitting. This can be achieved through techniques like backward elimination or by using algorithms like L1 regularization, which set less important feature weights to zero.

3. Cross-validation: This technique involves dividing the dataset into multiple subsets and training the model on different combinations of these subsets. By evaluating the model on the different test sets, we can get a more reliable estimate of its performance and prevent overfitting.

By implementing these techniques, we can effectively mitigate overfitting and achieve a more generalized and robust model.