Ovarian cancer is the leading cause of gynecologic cancer-related death in the United States. Historically, studies have demonstrated that ovarian cancer is a heterogeneous disease with several patient and oncologic characteristics, including BRCA status and residual disease at surgery, known to be predictive of clinical outcomes. However, over the last decade, the discovery and approval of bevacizumab and poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors have moved the front-line treatment paradigm beyond platinum-doublet therapy for women with advanced ovarian cancer. Subsequently, investigators have sought to assess the therapeutic efficacy of these agents in women who are considered "high" and "low" risk to determine which patients may benefit the most from aggressive therapy and in whom additional treatment may be avoided. We review historical and contemporary definitions of "high" and "low" risk ovarian cancer and how this has been incorporated into the subset analyses of randomized, clinical trials of therapeutic agents, including bevacizumab and PARP inhibitors. Next, we provide an in-depth discussion of landmark trials for front-line maintenance therapy with bevacizumab and/or PARP inhibitors, focusing on the impact of treatment efficacy according to a "high"- and "low"- risk paradigm. Further, we highlight that recent data have challenged this dichotomous classification, notably from the GOG-0218, ICON7, SOLO-1 and PAOLA-1 trials. While some studies have suggested that certain populations of women with advanced ovarian cancer may have a more favorable prognosis and be considered "low risk", the risk of progression and death remains unacceptably high in all women. Furthermore, in many cases, those considered lowest risk have the most treatment benefit from maintenance therapy with PARP inhibitors and/or bevacizumab. From this data, we advocate that that virtually all women with advanced ovarian cancer are high-risk and that use of our most effective therapies in the front-line setting holds promise for potentially curing more patients. Lastly, we critically discuss the practice of utilizing sub-analyses in clinical trials, with emphasis that while this practice is important for hypothesis generation, caution must be taken prior to accepting findings from sub-analyses as actual treatment effects.