Can you tell me what the Kernel trick is, in the context of SVMs?

The Kernel Trick, in the context of Support Vector Machines (SVM), is a mathematical technique that allows us to transform a linear classification problem into a high-dimensional nonlinear space, where the separator can be calculated more easily. This is achieved by defining a function called a kernel which maps the input data into a higher-dimensional space, where it can be separated using a linear separator.

On stackexchange for example https://stats.stackexchange.com/questions/308722/what-is-the-deeper-math-that-makes-the-kernel-trick-in-svms-work