Model-agnostic meta-learning (MAML) is a meta-learning algorithm that enables models to adapt quickly to new tasks with minimal data by optimizing for initial parameters that can be fine-tuned with just a few gradient steps. This approach is particularly effective in environments where tasks are similar but not identical, leveraging shared knowledge across tasks to improve learning efficiency.