How does SVM behave when kernel choice is incorrect?

Wrong kernel leads to poor decision boundary and underfitting or overfitting.

Kernel defines feature space transformation. A mismatch (e.g., linear kernel on nonlinear data) prevents proper separation, while overly complex kernels may overfit.