Linear separability refers to the ability of a dataset to be perfectly divided into distinct classes using a single linear boundary, such as a line in two dimensions or a hyperplane in higher dimensions. This property is crucial for the performance of linear classifiers like the Perceptron and Support Vector Machines, which rely on finding such boundaries to classify data points accurately.