One-shot learning is a machine learning technique where models make decisions based on a single data example, often used in scenarios like automated passport verification and facial recognition, where limited data is available for comparison. Unlike traditional models that require extensive datasets, one-shot learning involves neural networks like Siamese Neural Networks (SNNs) that compare similarities between images to provide a yes or no answer. This method is particularly useful in real-world applications requiring quick and accurate decisions, such as in security systems and biometric verification. One-shot learning, a subset of N-shot learning, is contrasted with few-shot and zero-shot learning, which involve slightly more data or none at all, respectively. It leverages deep learning models to function effectively with minimal data, making it ideal for environments where rapid and reliable decision-making is crucial.