Non-linear separability refers to the condition where data points cannot be divided into distinct classes using a straight line or hyperplane in their original space. Addressing this challenge often involves transforming the data into a higher-dimensional space where linear separation is feasible or utilizing algorithms capable of capturing complex patterns, like Support Vector Machines with kernel tricks or neural networks.