In very broad terms, PCA can be thought of as a pre-processing step to reduce the original data set to the "components" which account for the most variation in

In very broad terms, PCA can be thought of as a pre-processing step to reduce the original data set to the "components" which account for the most variation in the data. Essentially it's distilling your data down to what is most relevant, and thus helps say a classification algorithm work better by only training on the reduced "more manageable" data.