Nondeterministic Automata, or NFA, are theoretical models used in computer science to recognize patterns within input strings. Unlike deterministic automata, where each state has a single transition for each input symbol, NFAs can have multiple transitions for the same symbol, including transitions to multiple states or none at all. This allows NFAs to explore many possible paths simultaneously. In practice, NFAs are often used in regular expressions and lexical analysis for programming languages. They can be converted into equivalent deterministic automata, known as DFA, which can be easier to implement in software. Despite their complexity, NFAs are powerful tools for understanding computation.