How do you perform dimensionality reduction, and why is it essential in machine learning?

Dimensionality reduction techniques like PCA (Principal Component Analysis) reduce the number of features, making models faster and improving performance by removing noise. For example, Reducing 10 features to 2 main components while maintaining most of the information.