Feature engineering and dimensionality reduction are effective strategies to mitigate overfitting by enhancing the model's ability to generalize. Feature engineering improves the relevance of the data, while dimensionality reduction eliminates unnecessary noise. Increasing model complexity can exacerbate overfitting by making the model too tailored to the training data. Data augmentation, though useful, may not suffice alone. A comprehensive approach, including techniques like regularization and early stopping, is often necessary to effectively combat overfitting.