Linear Discriminant Analysis (LDA) is a feature transformation technique as well as a supervised classifier. It is commonly used as a preprocessing step for classification pipelines. The goal of LDA, like PCA, is to extract a new coordinate system and project datasets onto a lower-dimensional space. The main difference between LDA and PCA is that instead of focusing on the variance of the data as a whole like PCA, LDA optimizes the lower-dimensional space for the best class separability. This means that the new coordinate system is more useful in finding decision boundaries for classification models, which is perfect for us when building classification pipelines.
The reason that LDA is extremely useful is that separating based on class separability helps us avoid overfitting in our machine learning pipelines. This is also known as preventing the curse of dimensionality. LDA also reduces computational costs.