An algorithm's time complexity refers to the computational complexity that describes the amount of time an algorithm takes to run as a function of the length of the input. It provides insights into the algorithm's efficiency by predicting the time required to complete based on the size of the dataset. Time complexity is often expressed using Big O notation, such as O(n), O(log n), or O(n^2), indicating different growth rates associated with varying input sizes. Understanding time complexity helps developers select optimal algorithms for their specific tasks, enhancing overall application performance.