Time complexity is a way to measure how the runtime of an algorithm changes as the size of the input data increases. It helps us understand the efficiency of an algorithm by providing a high-level view of how long it will take to complete tasks based on the number of elements it processes. Common notations used to express time complexity include Big O notation, which describes the upper limit of an algorithm's running time. Different algorithms can have varying time complexities, such as O(1) for constant time, O(n) for linear time, and O(n^2) for quadratic time. By analyzing these complexities, developers can choose the most efficient algorithm for their needs, ensuring that applications run smoothly even with large datasets.