Dimensionality reduction involves techniques that transform high-dimensional data into a lower-dimensional form, making it easier to manage and process. Essential for tasks involving large datasets, these techniques retain the most crucial information while eliminating noise and redundancy. For instance, Principal Component Analysis (PCA) is a popular method that finds the key contributing dimensions and projects the data onto them. This is useful for visualization, storage efficiency, and speeding up algorithms.