The "bag-of-words" model is a simple way to represent text data for analysis. It treats a 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. This model is commonly used in natural language processing tasks like text classification and sentiment analysis. In the bag-of-words approach, documents are converted into numerical vectors, where each dimension corresponds to a specific word. This allows algorithms to process and analyze text data efficiently. However, it does not capture the context or meaning of words, which can limit its effectiveness in understanding complex language.