Support Vector Machine analysis - Python, how do I determing whether to use linear, square, or other types of SVM models? how do I determine whether to use linear, square, or other types of SVM models?

under which criteria should an SVM model be applied in the first place?
By Default, SVM in Sklearn uses RBF Kernel. You have to try out all the 3 kernels, with different Gamma and C.

SVM treats outliers better and add a penalty on every outlier it detects.

You should understand your data well or vizualize well to know what kernel fits well.
SVM literally adds a new dimension to the data to create a hyperplane.