CountVectorizer is a feature extraction tool in Natural Language Processing (NLP) that converts a collection of text documents into a matrix of token counts. It tokenizes the input text and builds a vocabulary of known words, then counts the occurrences of each word in the text and constructs a matrix where each row represents a document, and each column represents a word from the vocabulary.

This matrix can then be used as input to various machine learning models for text classification, sentiment analysis, and other NLP tasks. CountVectorizer can also include additional preprocessing steps such as removing stop words and performing stemming or lemmatization.