Physically motivated models of electromechanical motion systems are required in several applications related to control design, model-based fault detection and simply interpretation. Often, however, the high effort of modelling prohibits these model-based methods in industrial applications. Therefore, all approaches of automatic modelling / model selection are naturally appealing. An intuitive approach is to identify the parameters of several model candidates and to select the one with the best fit on unseen data. A shortcoming of this approach is that the chosen model may be one with high complexity in which some of the physically interpretable parameters are not practically identifiable and uncertain. Also, ambiguities in selecting the model structure would not be disguised resulting in false confidence in a chosen model. Designing a reasonable set of candidate models requires that distinguishability of models can be checked prior to the identification procedure.This paper proposes a strategy for frequency domain model selection. The resulting model is tailored to ensure practical identifiability of all parameters for the given excitation. The analysis is based on local sensitivity calculated for the frequency domain cost function. Also, the paper describes distinguishability analysis of candidate models utilizing transfer function coefficients and Markov parameters. Model selection and distinguishability analysis are applied to a class of models as they are commonly used to describe servo control systems. It is shown in experiments on an industrial stacker crane that model selection works with little user interaction, except from defining normalized hyperparameters and ensuring that the resulting model is sound. Distinguishability is analysed systematically for all models that result from rearranging actuator, sensor and spring-damper elements along a chain of discrete masses. It can be proved or disproved for almost all combinations of potential models.