Dimensionality reduction is the process of reducing the number of features in a dataset. This is important mainly in the case when you want to reduce variance in your model (overfitting).
Wikipedia states four advantages of dimensionality reduction (see here):
- It reduces the time and storage space required
- Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model
- It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D
- It avoids the curse of dimensionality