What issue does the pruning process in Decision Tree models aim to mitigate?

The pruning process in Decision Tree models is primarily aimed at mitigating overfitting. Overfitting occurs when a model captures the noise in the training data rather than the underlying pattern, resulting in poor generalization to unseen data. In the context of decision trees, overfitting can lead to excessively complex trees that branch out too far, creating numerous splits based on minor fluctuations in the training dataset.

Pruning addresses this issue by simplifying the tree after it has been fully grown. This is done by removing branches that provide little power in predicting the target variable, thus streamlining the model. By reducing the complexity of the model, pruning helps improve its performance on new, unseen data sets, resulting in better generalization and stability.

This ability to curb overfitting is crucial in developing models that maintain their predictive capabilities in real-world applications, hence making pruning an essential process in the life cycle of a decision tree model.