I think that the answer to your question is Yes (at least, in the big picture sense). Should you be wanting to dive deeper into details, I would suggest you to review this excellent discussion here on Cross Validated, especially an answer by @amoeba and/or Chapter 6 of the excellent online book by Revelle (2015). Having said that, I would like to make the following points:
Varimax and other rotation methods, are not specific to SPSS, as they are general exploratory factor analysis (EFA) terms (so maybe
spss tag should be deleted from the question).
While varimax is the most popular option across research literature (this is likely the reason it is the default option for
R) and usually produces simpler, easier to interpret, factor solutions, since all orthogonal rotation methods produce uncorrelated factors, they often are not the best. Oblique transformation methods, due to allowing factors to correlate, produce less simple models, however, it is argued that it is beneficial, since such models more accurately reflect reality, in other words, have higher explanatory power, with an additional benefit of better reproducibility of the results (Costello & Osborne, 2005).
I think that, following the tradition of the exploratory data analysis and research, it is much better to try several EFA approaches and methods and choose the optimal one, based not only on analytical fit indices, but first and foremost, based on making sense within the theory around studied constructs (if it exists) or domain knowledge (if developed theories don't yet exist for the domain under study).
Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7). Retrieved from http://pareonline.net/pdf/v10n7.pdf
Revelle, W. (2015). An introduction to psychometric theory with applications in R. [Website] Retrieved from http://www.personality-project.org/r/book