Kernel modification involves altering the kernel function in machine learning algorithms to improve performance or adapt to specific data characteristics. This process is crucial in kernel-based methods like Support Vector Machines, where the choice and modification of the kernel can significantly impact the model's ability to capture complex data patterns.