The "Bag of Words" (BoW) model is a simple and commonly used technique in natural language processing. It represents text data by treating each document as a collection of words, disregarding grammar and word order. Each unique word in the document is counted, creating a "bag" that reflects the frequency of each word's occurrence. This model is often used for tasks like text classification and sentiment analysis. By converting text into numerical vectors, it allows algorithms to process and analyze the data effectively. However, BoW can lose context and meaning, as it does not consider the relationships between words.