A Nondeterministic Finite Automaton (NFA) is a theoretical model used in computer science to represent and recognize patterns within input data. Unlike deterministic finite automata, NFAs can have multiple possible transitions for a given state and input symbol, allowing them to explore many paths simultaneously. This flexibility makes NFAs useful for certain types of pattern matching and language recognition. NFAs consist of states, transitions, and accepting states. They can transition to multiple states from a single state with the same input, or even transition without consuming any input (ε-transitions). Despite their complexity, NFAs can be converted into equivalent Deterministic Finite Automata (DFAs), which have a single unique transition for each state and input symbol.